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SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing

Author(s)
Ding, Qiliang; Somerville, Cherith; Manshaei, Roozbeh; Trost, Brett; Reuter, Miriam S.; Kalbfleisch, Kelsey; Stanley, Kaitlin; Okello, John B. A.; Hosseini, S. M.; Liston, Eriskay; Curtis, Meredith; Zarrei, Mehdi; Higginbotham, Edward J.; Chan, Ada J. S.; Engchuan, Worrawat; Thiruvahindrapuram, Bhooma; Scherer, Stephen W.; Kim, Raymond H.; Jobling, Rebekah K.; ... Show more Show less
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Abstract
Abstract Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP , a video tutorial series is available at https://bit.ly/SCIPVideos ).
Date issued
2022-11-14
URI
https://hdl.handle.net/1721.1/146556
Department
Sloan School of Management
Publisher
Springer Berlin Heidelberg
Citation
Ding, Qiliang, Somerville, Cherith, Manshaei, Roozbeh, Trost, Brett, Reuter, Miriam S. et al. 2022. "SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing."
Version: Final published version

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